arXiv subjects

Yue Yu

Publications and source records attributed to Yue Yu.

At least 433 records · Page 24Linked to original sources

arXiv2019

An Asymptotically Compatible Formulation for Local-to-Nonlocal Coupling Problems without Overlapping Regions

In this paper we design and analyze an explicit partitioned procedure for a 2D dynamic local-to-nonlocal (LtN) coupling problem, based on a new nonlocal Robin-type transmission condition. The nonlocal subproblem is modeled by the nonlocal heat equation with a finite horizon parameter $δ$ characterizing the range of nonlocal interactions, and the local subproblem is described by the classical heat equation. We consider a heterogeneous system where the local and nonlocal subproblems present different physical properties, and employ no overlapping region between the two subdomains. We first propose a new generalization of classical local Neumann-type condition by converting the local flux to a correction term in the nonlocal model, and show that the proposed Neumann-type boundary formulation recovers the local case as $O(δ^2)$ in the $L^{\infty}$ norm. We then extend the nonlocal Neumann-type boundary condition to a Robin-type boundary condition, and develop a local-to-nonlocal coupling formulation with Robin-Dirichlet transmission conditions. To stabilize the explicit coupling procedure and to achieve asymptotic compatibility, the choice of the coefficient in the Robin condition is obtained via amplification factor analysis for the discretized system with coarse grids. Employing a high-order meshfree discretization method in the nonlocal solver and a linear finite element method in the local solver, the selection of optimal Robin coefficients are verified with numerical experiments on heterogeneous and complicated domains. With the developed optimal coupling strategy, we numerically demonstrate the coupling framework's asymptotic convergence to the local limit with an $O(δ)=O(h)$ rate, when there is a fixed ratio between the horizon size $δ$ and the spatial discretization size $h$.

math.NA↗

arXiv2019

Modeling Long-Range Context for Concurrent Dialogue Acts Recognition

In dialogues, an utterance is a chain of consecutive sentences produced by one speaker which ranges from a short sentence to a thousand-word post. When studying dialogues at the utterance level, it is not uncommon that an utterance would serve multiple functions. For instance, "Thank you. It works great." expresses both gratitude and positive feedback in the same utterance. Multiple dialogue acts (DA) for one utterance breeds complex dependencies across dialogue turns. Therefore, DA recognition challenges a model's predictive power over long utterances and complex DA context. We term this problem Concurrent Dialogue Acts (CDA) recognition. Previous work on DA recognition either assumes one DA per utterance or fails to realize the sequential nature of dialogues. In this paper, we present an adapted Convolutional Recurrent Neural Network (CRNN) which models the interactions between utterances of long-range context. Our model significantly outperforms existing work on CDA recognition on a tech forum dataset.

eess.AS↗

arXiv2019

GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.

cs.CL↗

arXiv2019

Local Graph Stability in Exponential Family Random Graph Models

Exponential family Random Graph Models (ERGMs) can be viewed as expressing a probability distribution on graphs arising from the action of competing social forces that make ties more or less likely, depending on the state of the rest of the graph. Such forces often lead to a complex pattern of dependence among edges, with non-trivial large-scale structures emerging from relatively simple local mechanisms. While this provides a powerful tool for probing macro-micro connections, much remains to be understood about how local forces shape global outcomes. One simple question of this type is that of the conditions needed for social forces to stabilize a particular structure. We refer to this property as local stability and seek a general means of identifying the set of parameters under which a target graph is locally stable with respect to a set of alternatives. Here, we provide a complete characterization of the region of the parameter space inducing local stability, showing it to be the interior of a convex cone whose faces can be derived from the change-scores of the sufficient statistics vis-a-vis the alternative structures. As we show, local stability is a necessary but not sufficient condition for more general notions of stability, the latter of which can be explored more efficiently by using the ``stable cone'' within the parameter space as a starting point. In addition, we show how local stability can be used to determine whether a fitted model implies that an observed structure would be expected to arise primarily from the action of social forces, versus by merit of the model permitting a large number of high probability structures, of which the observed structure is one. We also use our approach to identify the dyads within a given structure that are the least stable, and hence predicted to have the highest probability of changing over time.

cs.DM↗

arXiv2019

Mass-spring-damper Networks for Distributed Optimization in Non-Euclidean Spaces

We consider the problem of minimizing the sum of non-smooth convex functions in non-Euclidean spaces, e.g., probability simplex, via only local computation and communication on an undirected graph. We propose two algorithms motivated by mass-spring-damper dynamics. The first algorithm uses an explicit update that computes subgradients and Bregman projections only, and matches the convergence behavior of centralized mirror descent. The second algorithm uses an implicit update that solves a penalized subproblem at each iteration, and achieves the iteration complexity of $\mathcal{O}(1/K)$. The results are also demonstrated via numerical examples.

math.OC↗

arXiv2019

An Asymptotically Compatible Approach For Neumann-Type Boundary Condition On Nonlocal Problems

In this paper we consider 2D nonlocal diffusion models with a finite nonlocal horizon parameter $δ$ characterizing the range of nonlocal interactions, and consider the treatment of Neumann-like boundary conditions that have proven challenging for discretizations of nonlocal models. While existing 2D nonlocal flux boundary conditions have been shown to exhibit at most first order convergence to the local counter part as $δ\rightarrow 0$, we present a new generalization of classical local Neumann conditions that recovers the local case as $O(δ^2)$ in the $L^{\infty}(Ω)$ norm. This convergence rate is optimal considering the $O(δ^2)$ convergence of the nonlocal equation to its local limit away from the boundary. We analyze the application of this new boundary treatment to the nonlocal diffusion problem, and present conditions under which the solution of the nonlocal boundary value problem converges to the solution of the corresponding local Neumann problem as the horizon is reduced. To demonstrate the applicability of this nonlocal flux boundary condition to more complicated scenarios, we extend the approach to less regular domains, numerically verifying that we preserve second-order convergence for domains with corners. Based on the new formulation for nonlocal boundary condition, we develop an asymptotically compatible meshfree discretization, obtaining a solution to the nonlocal diffusion equation with mixed boundary conditions that converges with $O(δ^2)$ convergence.

math.AP↗

arXiv2019

Magneto-optical Conductance of Kane Fermion Gas in Low Frequencies

Kane fermion is the counterpart of the Dirac fermion with pseudospin-1. Due to the existence of a bunch of gapless modes associated with Landau levels, the magnetic transport property of Kane fermion gas is very different from that of the Dirac semimetal. We calculate the magneto-optical conductance of the Kane fermion gas. We find that these gapless modes will contribute to a series of resonant peaks in low frequencies. We find that these peaks can explain the low frequency absorbance spectrum in a recent experiment for the Kane fermion material Hg$_{1-x}$Cd$_x$Te.

cond-mat.mes-hall↗

arXiv2019

A physically-consistent, flexible and efficient strategy to convert local boundary conditions into nonlocal volume constraints

Nonlocal models provide exceptional simulation fidelity for a broad spectrum of scientific and engineering applications. However, wider deployment of nonlocal models is hindered by several modeling and numerical challenges. Among those, we focus on the nontrivial prescription of nonlocal boundary conditions, or volume constraints, that must be provided on a layer surrounding the domain where the nonlocal equations are posed. The challenge arises from the fact that, in general, data are provided on surfaces (as opposed to volumes) in the form of force or pressure data. In this paper we introduce an efficient, flexible and physically consistent technique for an automatic conversion of surface (local) data into volumetric data that does not have any constraints on the geometry of the domain and on the regularity of the nonlocal solution and that is not tied to any discretization. We show that our formulation is well-posed and that the limit of the nonlocal solution, as the nonlocality vanishes, is the local solution corresponding to the available surface data. Quadratic convergence rates are proved for the strong energy and L-2 convergence. We illustrate the theory with one dimensional numerical tests whose results provide the ground work for realistic simulations.

math.AP↗

arXiv2019

Double Quantization for Communication-Efficient Distributed Optimization

Modern distributed training of machine learning models suffers from high communication overhead for synchronizing stochastic gradients and model parameters. In this paper, to reduce the communication complexity, we propose \emph{double quantization}, a general scheme for quantizing both model parameters and gradients. Three communication-efficient algorithms are proposed under this general scheme. Specifically, (i) we propose a low-precision algorithm AsyLPG with asynchronous parallelism, (ii) we explore integrating gradient sparsification with double quantization and develop Sparse-AsyLPG, (iii) we show that double quantization can also be accelerated by momentum technique and design accelerated AsyLPG. We establish rigorous performance guarantees for the algorithms, and conduct experiments on a multi-server test-bed to demonstrate that our algorithms can effectively save transmitted bits without performance degradation.

math.OC↗

arXiv2019

DAG-GNN: DAG Structure Learning with Graph Neural Networks

Learning a faithful directed acyclic graph (DAG) from samples of a joint distribution is a challenging combinatorial problem, owing to the intractable search space superexponential in the number of graph nodes. A recent breakthrough formulates the problem as a continuous optimization with a structural constraint that ensures acyclicity (Zheng et al., 2018). The authors apply the approach to the linear structural equation model (SEM) and the least-squares loss function that are statistically well justified but nevertheless limited. Motivated by the widespread success of deep learning that is capable of capturing complex nonlinear mappings, in this work we propose a deep generative model and apply a variant of the structural constraint to learn the DAG. At the heart of the generative model is a variational autoencoder parameterized by a novel graph neural network architecture, which we coin DAG-GNN. In addition to the richer capacity, an advantage of the proposed model is that it naturally handles discrete variables as well as vector-valued ones. We demonstrate that on synthetic data sets, the proposed method learns more accurate graphs for nonlinearly generated samples; and on benchmark data sets with discrete variables, the learned graphs are reasonably close to the global optima. The code is available at \url{https://github.com/fishmoon1234/DAG-GNN}.

cs.LG↗

arXiv2019

Stochastic Bregman Parallel Direction Method of Multipliers for Distributed Optimization

Bregman parallel direction method of multipliers (BPDMM) efficiently solves distributed optimization over a network, which arises in a wide spectrum of collaborative multi-agent learning applications. In this paper, we generalize BPDMM to stochastic BPDMM, where each iteration only solves local optimization on a randomly selected subset of nodes rather than all the nodes in the network. Such generalization reduce the need for computational resources and allows applications to larger scale networks. We establish both the global convergence and the $O(1/T)$ iteration complexity of stochastic BPDMM. We demonstrate our results via numerical examples.

math.OC↗

arXiv2019

Tolling for Constraint Satisfaction in Markov Decision Process Congestion Games

Markov decision process (MDP) congestion game is an extension of classic congestion games, where a continuous population of selfish agents solves Markov decision processes with congestion: the payoff of a strategy decreases as more population uses it. We draw parallels between key concepts from capacitated congestion games and MDP. In particular, we show that population mass constraints in MDP congestion games are equivalent to imposing tolls/incentives on the reward function, which can be utilized by social planners to achieve auxiliary objectives. We demonstrate such methods in a simulated Seattle ride-share model, where tolls and incentives are enforced for two separate objectives: to guarantee minimum driver density in downtown Seattle, and to shift the game equilibrium towards a maximum social output.

cs.GT↗

arXiv2019

Exploring Fast and Communication-Efficient Algorithms in Large-scale Distributed Networks

The communication overhead has become a significant bottleneck in data-parallel network with the increasing of model size and data samples. In this work, we propose a new algorithm LPC-SVRG with quantized gradients and its acceleration ALPC-SVRG to effectively reduce the communication complexity while maintaining the same convergence as the unquantized algorithms. Specifically, we formulate the heuristic gradient clipping technique within the quantization scheme and show that unbiased quantization methods in related works [3, 33, 38] are special cases of ours. We introduce double sampling in the accelerated algorithm ALPC-SVRG to fully combine the gradients of full-precision and low-precision, and then achieve acceleration with fewer communication overhead. Our analysis focuses on the nonsmooth composite problem, which makes our algorithms more general. The experiments on linear models and deep neural networks validate the effectiveness of algorithms.

math.OC↗

arXiv2019

Fragile superconductivity in the presence of weakly disordered charge density waves

When superconducting (SC) and charge-density wave (CDW) orders compete, novel low temperature behaviors can result. From an analysis of the Landau-Ginzberg-Wilson theory of competing orders, we demonstrate the generic occurrence of a `fragile' SC phase at low temperatures and high fields in the presence of weak disorder. Here, the SC order is largely concentrated in the vicinity of dilute dislocations in the CDW order, leading to transition temperatures and critical currents that are parametrically smaller than those characterizing the zero field SC phase. This may provide the outline of an explanation of the recently discovered `resilient' superconducting phase at high fields in underdoped YBa$_2$Cu$_3$O$_{6+δ}$.

cond-mat.supr-con↗

arXiv2019

Effect of strain inhomogeneity on a chiral p-wave superconductor

Motivated by recent measurements of strain effects on the transition temperature (T$_c$) of Sr$_2$RuO$_4$, we study the strain response of a two-dimensional chiral p-wave supercoductor. We focus on the effects of inhomgeneous strain fields, which are always present in any such experiment, and which have been neglected in previous theoretical treatments. We show that the response of T$_c$ of a chiral superconductor to strain chages from being linear without inhomogeneity, to quadratic in the presence of inhomogeneity. We discuss our results in the context of the ongoing debate of superconductivity in Sr$_2$RuO$_4$.

cond-mat.supr-con↗

arXiv2019

Anomalous magnetic transport and extra quantum oscillation in semi-metallic photon-like fermion gas

In the absence of Lorentz symmetry, the pseudospin-1 counterpart of Weyl fermion (feroton) with linear dispersions and an exact flat band can emerge in condensed matter systems. The flat band branch of feroton is equivalent to the longitudinal photon in Maxwell theory, which is a redundant degree of freedom due to the emergent (fermionic) gauge symmetry. Upon coupling to an external magnetic field, the fermionic guage symmetry is broken and the flat band ferotons become gapless excitations characterized by Landau level indices ($n>1$). In the long wave length limit, these gapless modes are of the opposite chirality to the chiral anomaly related zero Landau level, which leads to much more plentiful magnetic transport properties. To further explore the novel properties of these gapless modes, we investigate the quantum oscillation through a generalized Lieb lattice model. We find an extra oscillating behavior which indicates the existence of these exotic gapless modes. We collect known {\it ab initio} calculation data from literature and discuss the possibility of realizing the semi-metallic feroton gas in real materials.

cond-mat.mes-hall↗

arXiv2018

Field Induced Oscillation of Two Majorana Modes in a Quantum Ring

We calculate the topological boundary modes found for quantum wire for a quantum ring. For the symmetric ring we find analytical solutions for two quasi-particles identifiable as the Majorana states and for asymmetric ring, we have also find approximate solution. By turning on a small field at time zero, we find a field induced oscillation of the Majorana states that was initially localized. The range of validity of our solution, the relation between the period of oscillation and the coin parameter and field strength, and the encoding of a message of the on/off of the field using the Majorana states are discussed. Our results suggest ways to reduce decoherence for small systems, thereby providing a good candidate for quantum memory and computing as the Majorana qubit is topologically protected.

quant-ph↗

arXiv2018

An Initial Step Towards Organ Transplantation Based on GitHub Repository

Organ transplantation, which is the utilization of codes directly related to some specific functionalities to complete ones own program, provides more convenience for developers than traditional component reuse. However, recent techniques are challenged with the lack of organs for transplantation. Hence, we conduct an empirical study on extracting organs from GitHub repository to explore transplantation based on large-scale dataset. We analyze statistics from 12 representative GitHub projects and get the conclusion that 1) there are abundant practical organs existing in commits with add as a key word in the comments; 2) organs in this repository mainly possess four kinds of contents; 3) approximately 70% of the organs are easy-to-transplant. Implementing our transplantation strategy for different kinds of organs, we manually extract 30 organs in three different programming languages, namely Java, Python, and C, and make unit tests for them utilizing four testing tools (two for Java, one for Python, and one for C). At last, we transplant three Java organs into a specific platform for a performance check to verify whether they can work well in the new system. All the 30 organs extracted by our strategy possess good performances in unit test with the highest passing rate reaching 97% and the lowest one still passing 80% and the three Java organs work well in the new system, providing three new functionalities for the host. All the results indicate the feasibility of organ transplantation based on open-source repository, bringing new idea for code reuse.

cs.SE↗